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Predicting student performance using clickstream data and machine learning

Citation

Liu, Y and Fan, Si and Xu, S and Sajjanhar, A and Yeom, S and Wei, Y, Predicting student performance using clickstream data and machine learning, Educational Sciences, 13, (1) Article 17. ISSN 2227-7102 (2023) [Refereed Article]


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DOI: doi:10.3390/educsci13010017

Abstract

Student performance predictive analysis has played a vital role in education in recent years. It allows for the understanding studentsí learning behaviours, the identification of at-risk students, and the development of insights into teaching and learning improvement. Recently, many researchers have used data collected from Learning Management Systems to predict student performance. This study investigates the potential of clickstream data for this purpose. A total of 5341 sample students and their click behaviour data from the OULAD (Open University Learning Analytics Dataset) are used. The raw clickstream data are transformed, integrating the time and activity dimensions of studentsí click actions. Two feature sets are extracted, indicating the number of clicks on 12 learning sites based on weekly and monthly time intervals. For both feature sets, the experiments are performed to compare deep learning algorithms (including LSTM and 1D-CNN) with traditional machine learning approaches. It is found that the LSTM algorithm outperformed other approaches on a range of evaluation metrics, with up to 90.25% accuracy. Four out of twelve learning sites (content, subpage, homepage, quiz) are identified as critical in influencing student performance in the course. The insights from these critical learning sites can inform the design of future courses and teaching interventions to support at-risk students.

Item Details

Item Type:Refereed Article
Keywords:Machine Learning; Learning Analytics; Educational Data Mining; student performance prediction; clickstream data
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Modelling and simulation
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Liu, Y (Miss Yutong Liu)
UTAS Author:Fan, Si (Dr Frances Fan)
UTAS Author:Xu, S (Dr Shuxiang Xu)
UTAS Author:Yeom, S (Dr Soonja Yeom)
UTAS Author:Wei, Y (Dr Yuchen Wei)
ID Code:155196
Year Published:2023
Deposited By:Information and Communication Technology
Deposited On:2023-02-02
Last Modified:2023-02-02
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